self-paced
Introduction to AI
Build a clear, practical foundation in what artificial intelligence is, how it differs from traditional software, and why it matters for modern organisations. This beginner-friendly course explains core AI concepts, common business use cases, future directions, and the importance of responsible governance.
- Duration
- 60–90 minutes
- Price
- £1.00
- Self-paced lessons
- Assessment
- Credential
Certificate
Introduction to AI Certificate
Awarded on successful completion of all required course steps.
Course syllabus
Lessons are grouped into clear steps with visual cues, so each course is easier to scan before you start.
Lessons unlock after purchase. Create your account with the same email you paid with — no course code is required — or choose “Already purchased?” above.
- Step 1 · Lesson
What Is AI?
This lesson defines AI as a field of computer science focused on enabling machines to perform tasks that usually require human intelligence, including decision-making support, problem-solving, natural language processing and pattern recognition. Learners explore simple business examples such as fraud detection, chatbots, image recognition and recommendation engines.
- Step 2 · Lesson
AI Versus Traditional Software
This lesson compares traditional rule-based software, which follows fixed instructions, with adaptive AI systems that learn from data and improve or adjust outputs over time. Learners examine why this difference matters in business settings where patterns, behaviour and data change frequently.
- Step 3 · Lesson
AI, Machine Learning, Deep Learning and Neural Networks
This lesson explains the relationship between the key terms: AI is the broadest category, machine learning is a subset where systems learn patterns from data, and deep learning uses neural networks with many layers. Learners also discover that neural networks are model structures inspired by connected nodes, rather than literal copies of the human brain.
- Step 4 · Lesson
What Is an AI Model?
This lesson explains that an AI model maps inputs to outputs, where inputs might include text, images, audio or structured data, and outputs might include summaries, classifications, generated text, images, audio or actions. Learners also clarify that AI models do not simply look up answers like a database; they generate outputs based on training patterns and the context provided.
- Step 5 · Assessment
Introduction to AI final test
Final assessment covering core AI concepts, differences between traditional software and machine learning, common business use cases, and key AI limitations such as hallucinations and bias.